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As detailed in https://arxiv.org/pdf/2107.04092.pdf one can slice the rows of the connectivity matrix into vertical slices where each slice addresses a defined slice of post-synaptic neurons. Pre-loading these neurons' relevant variables into shared memory and using shared atomics for inSyn updates reduces global memory traffic leading to measurable performance gains.
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neworderofjamie
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Improve spike delivery for sparse connections using shared memory and shared atomics
Improve presynaptic updates using square blocks and shared memory
Jul 15, 2021
Square blocks might also lets us improve the performance of spike processing in all postsynaptically-parallelised strategies by using threads in y to process multiple spikes in parallel. For sparse matrices these can atomically update inSyn in global/shared memory and for dense matrices they can update registers and then atomic add the partial sums or use warp shuffles to sum and then add.
As detailed in https://arxiv.org/pdf/2107.04092.pdf one can slice the rows of the connectivity matrix into vertical slices where each slice addresses a defined slice of post-synaptic neurons. Pre-loading these neurons' relevant variables into shared memory and using shared atomics for inSyn updates reduces global memory traffic leading to measurable performance gains.
The text was updated successfully, but these errors were encountered: